Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning

Okada, Natsuo; Sinaice, Brian Bino; Kim, Jaewon; Nozaki, Hiromasa; Takizawa, Kaito; Owada, Narihiro; Ohtomo, Yoko; Kawamura, Youhei

Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning

Okada, Natsuo; Sinaice, Brian Bino; Kim, Jaewon et al.

International Journal of Mining, Reclamation and Environment, 2024, 38(10), 851-871

Permalink : https://hdl.handle.net/2115/95379

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/95379
タイトル	Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning
著者	Okada, Natsuo ; Sinaice, Brian Bino ; Kim, Jaewon ; Nozaki, Hiromasa ; Takizawa, Kaito ; Owada, Narihiro ; Ohtomo, Yoko ; Kawamura, Youhei
著者名 Okada, Natsuo
著者名 Sinaice, Brian Bino
著者名 Kim, Jaewon
著者名 Nozaki, Hiromasa
著者名 Takizawa, Kaito
著者名 Owada, Narihiro
e-Rad_Researcher 80612902 著者名 Ohtomo, Yoko
e-Rad_Researcher 40361323 著者名 Kawamura, Youhei
言語	英語
キーワード	Hyperspectral imaging
	MATLAB
	spectroscopy
	mineral processing
発行日	2024-11-25
出版者	Taylor & Francis
収録物名	International Journal of Mining, Reclamation and Environment
巻(号)	38(10)
ページ	851-871
抄録	The study presents a new technique that combines hyperspectral imaging and machine learning to identify minerals. Overcoming the challenges of processing hyperspectral data, the solution offers a user-friendly hyperspectral analysis tool tailored for constructing datasets and enhancing mineral identification and concentration estimation. The tool integrates hyperspectral data processing with segmentation, simplifying complex operations and making mineral identification accessible to non-experts. The tool's capabilities extend to handling multi-spectral data efficiently, potentially leading to energy-efficient analysis when combined with dimensionality reduction techniques. Presented as a novel approach, it improves geological surveys in mining areas, enabling industrial applications and mineral research.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Mining, Reclamation and Environment on 25 November 2024, available at: https://doi.org/10.1080/17480930.2024.2360743.
PISSN	1748-0930
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1080/17480930.2024.2360743